Parameter settings: The optimal parameters of the above algorithms are set by experimental verification or the conclusions obtained of the original paper [1]}, [2]}, [3]}, [4]}, [5]}, [6]}. For URIR, we use Adam as optimizer, and use mini-batch gradient descent algorithm. These methods are some commonly used optimization strategies [5]}, [8]}, [6]}. In view of the influence of different hyperparameters the embedding dimension of entity d, the number of neighbors collected k, the order of the high-level neighbor L and the number of elements of \(H_u\)  n, we also made a comprehensive comparison of the system. The learning rate, batch size, and L2 regularization coefficient are determined according to training time. The initialization parameters of the model are: d=4, k=4, L=2, n=5. Hyperparameter adjustment sequence is d, L, k, n. The detailed comparison process and comparison results are introduced in Section REF . The default values of these parameters are shown in Table REF .
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